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Multi-Class Protein Fold Recognition using Large Margin Logic based Divide and Conquer Learning
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BibTeX
@MISC{Lodhi_multi-classprotein,
author = {Huma Lodhi and Stephen Muggleton and Mike J. E. Sternberg},
title = {Multi-Class Protein Fold Recognition using Large Margin Logic based Divide and Conquer Learning},
year = {}
}
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Abstract
Inductive Logic Programming (ILP) systems have been successfully applied to solve complex problems in bioinformatics by viewing them as binary classification tasks. It remains an open question how an accurate solution to a multi-class problem can be obtained by using a logic based learning method. In this paper we present a novel logic based approach to solve complex and challenging multi-class classification problems by focusing on a key task, namely protein fold recognition. Our technique is based on the use of large margin methods in conjunction with the kernels constructed from first order rules induced by an ILP system. The proposed approach learns a multi-class classifier by using a divide and conquer reduction strategy that splits multi-classes into binary groups and solves each individual problem recursively hence generating an underlying decision list structure. The method is applied to assigning protein domains to folds. Experimental evaluation of the method demonstrates the efficacy of the proposed approach to solving multi-class classification problems in bioinformatics. 1.
Keyphrases
conquer learning large margin logic multi-class protein fold recognition multi-class classification problem underlying decision list structure binary classification task novel logic multi-class classifier key task experimental evaluation multi-class problem protein fold recognition conquer reduction strategy open question binary group accurate solution inductive logic programming protein domain complex problem first order rule individual problem ilp system large margin method
